arXiv research

The study examines how class imbalance affects precision-recall curves.

Improves interpretability of neural network intermediate layers by making concept activations more robust and effective.

Paper proposes a human-algorithm approach to reduce medical device recall risk and workload.

In this article we revisit the definition of Precision-Recall (PR) curves for generative models proposed by Sajjadi et al. (arXiv:1806.00035). Rather than providing a scalar for generative quality, PR curves distinguish mode-collapse (poor recall) and bad quality (poor precision). We first generalize their formulation …

Mobile app improves speech recognition of names with user feedback.

In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multi-source fusion scheme in Keyword Spotting (KWS) System for vehicle applications. Vehicle information, as a new source for the original system, is collected by an in-vehicle data acquisition platform while th…

Transformers can store facts efficiently using associative memories.

Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem. The problem becomes particularly acute when we consider incremental classifiers operating on imbalanced data streams, especially when the l…

A new method for optimizing non-decomposable metrics with constraints.

Capsule Networks improve clothing retrieval without landmark info.

PAGE optimizes nonconvex problems with optimal convergence rates.

Transformers recall from long distributions with statistical guarantees.

We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independ…

Study improves detection of cryptocurrency pump-and-dump schemes.

Method minimizes total cost of classification by acquiring covariates efficiently.

New algorithm reduces online learning regret for bounded recall games.

This paper introduces a new method to train normalizing flows using precision-recall divergences.

We obtain expressions for the shear and the vorticity tensors of perfect-fluid spacetimes, in terms of the divergence of the Weyl tensor. For such spacetimes, we prove that if the gradient of the energy density is parallel to the velocity, then either the expansion rate is zero, or the vorticity vanishes. This statemen…

We consider a network of agents that aim to learn some unknown state of the world using private observations and exchange of beliefs. At each time, agents observe private signals generated based on the true unknown state. Each agent might not be able to distinguish the true state based only on her private observations.…

DMF improves POI recommendation privacy and efficiency.

Graphical models have gained a lot of attention recently as a tool for learning and representing dependencies among variables in multivariate data. Often, domain scientists are looking specifically for differences among the dependency networks of different conditions or populations (e.g. differences between regulatory …

Algorithm learns NE in imperfect information games with imperfect feedback.

We show that many machine learning goals, such as improved fairness metrics, can be expressed as constraints on the model's predictions, which we call rate constraints. We study the problem of training non-convex models subject to these rate constraints (or any non-convex and non-differentiable constraints). In the non…

The study formalizes temporal precision and recall for anomaly detection in sequences.

Solves the Sleeping Beauty problem as a 'thirder' using the Kelly Criterion.

Unified and extended precision-recall metrics for generative models.

For information retrieval and binary classification, we show that precision at the top (or precision at k) and recall at the top (or recall at k) are maximised by thresholding the posterior probability of the positive class. This finding is a consequence of a result on constrained minimisation of the cost-sensitive exp…

Recalls intrinsically harmonic forms and open problems.

Paper tackles imbalanced binary classification by optimizing precision and recall directly.

LLMs can memorize economic data and recall exact values before their training cutoff.

Bayesian Optimization improves machine learning for detecting network attacks.

RAGuard improves safety in LLMs for offshore wind maintenance.

A new algorithm LONR learns without terminal states or perfect recall.

Framework for precise recall control in spatial conflation tasks.

A new method for generating replay samples on the fly, optimizing for not forgetting.

Proposes a new tree-based algorithm for class-imbalanced data.

The paper critiques and expands on common evaluation metrics in machine learning.

New algorithm improves similarity graph construction for nearest neighbor search.

New findings show tool-augmented models can recall unlimited facts, outperforming purely memorized models.

In most machine learning applications, classification accuracy is not the primary metric of interest. Binary classifiers which face class imbalance are often evaluated by the $F_β$ score, area under the precision-recall curve, Precision at K, and more. The maximization of many of these metrics can be expressed as a con…

The optimal ranking score between precision and recall is rarely F1 and can be found using specific methods.

Recalls and refines the concept of algebraically rectifiable curves.

Study certifies missed relevant items in candidate generation with audit labels.

DIAL learns embeddings to maximize recall and accuracy for entity resolution.

Short sales are regarded as negative purchases in textbook asset pricing theory. In reality, however, the symmetry between purchases and short sales is broken by a variety of costs and risks peculiar to the latter. We formulate an optimal stopping model in which the decision to cover a short position is affected by two…

Despite the tremendous progress in the estimation of generative models, the development of tools for diagnosing their failures and assessing their performance has advanced at a much slower pace. Recent developments have investigated metrics that quantify which parts of the true distribution is modeled well, and, on the…

In this article we propose a novel ranking algorithm, referred to as HierLPR, for the multi-label classification problem when the candidate labels follow a known hierarchical structure. HierLPR is motivated by a new metric called eAUC that we design to assess the ranking of classification decisions. This metric, associ…

Enhanced anomaly detection using PRC-RF with autoencoders.